Signal processing is an intrinsic procedure of any measurement technique, and always requires sufficient signal-to-noise ratio to enable extraction of a signal component indicative of a desired parameter from a noise component contained in the measured signal. For example, measurement techniques aimed at determining physiological parameters consist of detecting and analyzing a signal response, e.g., light response, of a sample to the application of an external field, e.g., electromagnetic radiation, and typically require suitable signal processing to extract the signal component in the detected response.
Various techniques for non-invasive measurements of blood parameters have been developed. One of such techniques is the so-called “bio-impedance technique” consisting of the following. A current source produces an alternating current, which is applied to the body through electrodes, and voltage induced by this current passage through the body is measured at additional electrodes. Other techniques utilize spectrophotometry consisting of illumination of a body part by incident light of various wavelengths and measurement of an absorption spectrum.
The most popular spectrophotometric techniques are oximetry and pulse oximetry. Oximetry is based on the strong dependence of the optical property of blood in the visible (between 500 and 700 nm) and near-infrared (between 700 and 1000 nm) spectra on the amount of oxygen in blood. Pulse oximetry, which utilizes transmission and reflection modes, relies on the detection of a photoplethysmographic signal caused by variations in the quantity of arterial blood associated with periodic contraction and relaxation of a patient's heart. The magnitude of this signal depends on the amount of blood ejected from the heart into the peripheral vascular bed with each systolic cycle, the optical absorption of the blood, absorption by skin and tissue components, and the specific wavelengths that are used to illuminate the tissue. Oxyhemoglobin saturation (SaO2) is determined by computing the relative magnitudes of red (R) and infrared (IR) photoplethysmograms.
Electronic circuits, or suitable software (algorithm) inside the pulse oximeter, separate the R and IR photoplethysmograms into their respective pulsatile (AC) and non-pulsatile (DC) signal components. An algorithm inside the pulse oximeter performs a mathematical normalization by which the time-varying AC signal at each wavelength is divided by the corresponding time-invariant DC component which results mainly from the light absorbed and scattered by the bloodless tissue, residual arterial blood when the heart is in diastole, venous blood and skin pigmentation. Since it is assumed that the AC portion results only from the arterial blood component, this scaling process provides a normalized R/IR ratio, i.e., the ratio of AC/DC values corresponding to R- and IR-spectrum wavelengths, respectively, which is highly dependent on SaO2, but is largely independent of the volume of arterial blood entering the tissue during systole, skin pigmentation, skin thickness arid vascular structure.
Pulse oximetry operating in reflection mode, while being based on similar spectrophotometric principles as that of transmission mode, is more challenging to perform and has unique problems that cannot always be solved by solutions suitable for solving the problems associated with transmission-mode pulse oximetry. Generally, when comparing transmission and reflection pulse oximetry, the problems associated with reflection pulse oximetry consist of the following. In reflection pulse oximetry, the pulsatile AC signals are generally very small and, depending on sensor configuration and placement, have larger DC components as compared to those of transmission pulse oximetry. In addition to optical absorption and reflection due to blood, the DC signal of the R and IR photoplethysmograms in reflection pulse oximetry can be adversely affected by strong reflections from a bone. This problem becomes more apparent when applying measurements at such body locations as the forehead and the scalp, or when the sensor is mounted on the chest over the ribcage. Similarly, variations in contact pressure between the sensor and the skin can cause larger errors in reflection pulse oximetry (as compared to transmission pulse oximetry) since some of the blood near the superficial layers of the skin may be normally displaced away from the sensor housing towards deeper subcutaneous structures. Consequently, the highly reflective bloodless tissue compartment near the surface of the skin can cause significant errors even at body locations where the bone is located too far away to influence the incident light generated by the sensor.
Another problem with reflectance sensors currently available is the potential for specular reflection caused by the superficial layers of the skin, when an air gap exists between the sensor and the ski, or by the direct shunting of light between the LEDs and the photodetector through a thin layer of fluid (which may be due to excessive sweating or from amniotic fluid present during delivery).
It is important to keep in mind the two fundamental assumptions underlying conventional dual-wavelength pulse oximetry: The path of light rays with different illuminating wavelengths in tissue are substantially equal, and therefore, cancel each other. Each light source illuminates the same pulsatile change in arterial blood volume. Furthermore, the correlation between optical measurements and tissue absorption in pulse oximetry are based on the fundamental assumption that light propagation is determined primarily by absorbance due to Lambert-Beer's law neglecting multiple scattering effects in biological tissues. In practice, however, the optical paths of different wavelengths in biological tissues are known to vary more in reflectance oximetry compared to transmission oximetry, since they strongly depend on the light scattering properties of the illuminated tissue and sensor mounting.
The relevant in vivo studies are disclosed, for example, in the following publications:
Dassel, et al., “Effect of location of the sensor on reflectance pulse oximetry”, British Journal of Obstetrics and Gynecology, vol. 104, pp. 910–916, (1997);
Dassel, et al., “Reflectance pulse oximetry at the forehead of newborns: The influence of varying pressure on the probe”, Journal of Clinical Monitoring, vol. 12, pp. 421–428, (1996).
It should be understood that the signal-to-noise ratio improvement is also needed in tissue simulated model measurements (in vitro). The problems arising with in vitro measurements are disclosed, for example in the following publication: Edrich et al., “Fetal pulse, oximetry: influence of tissue blood content and hemoglobin concentration in a new in-vitro model”, European Journal of Obstetrics and Gynecology and Reproductive Biology, vol. 72, suppl. 1, pp. S29–S34, (1997).
Improved sensors for application in dual-wavelength reflectance pulse oximetry have been developed, and are disclosed, for example, in the following publication: Mendelson, et al., “Noninvasive pulse oximetry utilizing skin reflectance photoplethysmography”, IEEE Transactions on Biomedical Engineering, vol. 35, no. 10, pp. 798–805 (1988). According to this technique, the total amount of backscattered light that can be detected by a reflectance sensor is directly proportional to the number of photodetectors placed around the LEDs. Additional improvements in signal-to-noise ratio were achieved by increasing the active area of the photodetector and optimizing the separation distance between the light sources and photodetectors.
A different approach, based on the use of a sensor having six photodiodes arranged symmetrically around the LEDs, is disclosed in the following publications:                Mendelson, et al., “Design and evaluation of a new reflectance pulse oximeter sensor”, Medical Instrumentation, vol. 22, no. 4, pp. 167–173 (1988); and        Mendelson, et al., “Skin reflectance pulse oximetry: in vivo measurements from the forearm and calf”, Journal of Clinical Monitoring, vol. 7, pp. 7–12, (1991).        
According to this approach, in order to maximize the fraction of backscattered light collected by the sensor, the currents from all six photodiodes are summed electronically by internal circuitry in the pulse oximeter. This configuration essentially creates a large area photodetector made of six discrete photodiodes connected in parallel to produce a single current that is proportiorial to the amount of light backscattered from the skin.
A reflectance sensor based on the use of eight dual-wavelength LEDs and a single photodiode is disclosed in the following publication: Takatani et al., “Experimental and clinical evaluation of a noninvasive reflectance pulse oximeter sensor”, Journal of Clinical Monitoring, vol. 8, pp. 257–266 (1992). Here, four R and four IR LEDs are spaced at 90-degree intervals around the substrate and at an equal radial distance from the photodiode. A similar sensor configuration based on six photodetectors mounted in the center of the sensor around the LEDs is disclosed in the following publication: Konig, et al., “Reflectance pulse oximetry—principles and obstetric application in the Zurich system”, Journal of Clinical Monitoring, vol. 14, pp. 403–412 (1998).
Pulse oximeter probes of the type comprising three or more LEDs for filtering noise and monitoring other functions, such as carboxyhemoglobin or various indicator dyes injected into the blood stream, have been developed and are disclosed, for example, in WO 00/32099 and U.S. Pat. No. 5,842,981. The techniques disclosed in these publications are aimed at providing an improved method for direct digital signal formation from input signals produced by the sensor and for filtering noise.
As indicated above, in pulse oximetry, SpO2 and the heart rate are calculated from the detected signal, which is relatively small with a reflection-mode pulse oximeter. Methods for processing the signals detected by a pulse oximeter are described in the following U.S. Pat. Nos. 5,482,036; 5,490,505; 5,685,299; 5,632,272; 5,769,785; 6,036,642; 6,081,735; 6,067,462; and 6,083,172. These methods, however, utilize a specific model based on certain assumptions of noise reference.